The E-Textile for Biomedical Applications: A Systematic Review of Literature.
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ABSTRACT:
SUBMITTER: Cesarelli G
PROVIDER: S-EPMC8700039 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Polymeric nanomaterials have become a prominent area of research in the field of drug delivery. Their application in nanomedicine can improve bioavailability, pharmacokinetics, and, therefore, the effectiveness of various therapeutics or contrast agents. There are many studies for developing new polymeric nanocarriers; however, their clinical application is somewhat limited. In this review, we present new complex and multifunctional polymeric nanocarriers as promising and innovative diagnostic or therapeutic systems. Their multifunctionality, resulting from the unique chemical and biological properties of the polymers used, ensures better delivery, and a controlled, sequential release of many different therapeutics to the diseased tissue. We present a brief introduction of the classical formulation techniques and describe examples of multifunctional nanocarriers, whose biological assessment has been carried out at least in vitro. Most of them, however, also underwent evaluation in vivo on animal models. Selected polymeric nanocarriers were grouped depending on their medical application: anti-cancer drug nanocarriers, nanomaterials delivering compounds for cancer immunotherapy or regenerative medicine, components of vaccines nanomaterials used for topical application, and lifestyle diseases, ie, diabetes.
| S-EPMC7654520 | biostudies-literature
Project description:In the scientific literature, spin refers to reporting practices that distort the interpretation of results and mislead readers so that results are viewed in a more favourable light. The presence of spin in biomedical research can negatively impact the development of further studies, clinical practice, and health policies. This systematic review aims to explore the nature and prevalence of spin in the biomedical literature. We searched MEDLINE, PreMEDLINE, Embase, Scopus, and hand searched reference lists for all reports that included the measurement of spin in the biomedical literature for at least 1 outcome. Two independent coders extracted data on the characteristics of reports and their included studies and all spin-related outcomes. Results were grouped inductively into themes by spin-related outcome and are presented as a narrative synthesis. We used meta-analyses to analyse the association of spin with industry sponsorship of research. We included 35 reports, which investigated spin in clinical trials, observational studies, diagnostic accuracy studies, systematic reviews, and meta-analyses. The nature of spin varied according to study design. The highest (but also greatest) variability in the prevalence of spin was present in trials. Some of the common practices used to spin results included detracting from statistically nonsignificant results and inappropriately using causal language. Source of funding was hypothesised by a few authors to be a factor associated with spin; however, results were inconclusive, possibly due to the heterogeneity of the included papers. Further research is needed to assess the impact of spin on readers' decision-making. Editors and peer reviewers should be familiar with the prevalence and manifestations of spin in their area of research in order to ensure accurate interpretation and dissemination of research.
| S-EPMC5593172 | biostudies-literature
Project description:BackgroundThere is an increasing need to evaluate the production and impact of medical research produced by institutions. Many indicators exist, yet we do not have enough information about their relevance. The objective of this systematic review was (1) to identify all the indicators that could be used to measure the output and outcome of medical research carried out in institutions and (2) enlist their methodology, use, positive and negative points.MethodologyWe have searched 3 databases (Pubmed, Scopus, Web of Science) using the following keywords: [Research outcome* OR research output* OR bibliometric* OR scientometric* OR scientific production] AND [indicator* OR index* OR evaluation OR metrics]. We included articles presenting, discussing or evaluating indicators measuring the scientific production of an institution. The search was conducted by two independent authors using a standardised data extraction form. For each indicator we extracted its definition, calculation, its rationale and its positive and negative points. In order to reduce bias, data extraction and analysis was performed by two independent authors.FindingsWe included 76 articles. A total of 57 indicators were identified. We have classified those indicators into 6 categories: 9 indicators of research activity, 24 indicators of scientific production and impact, 5 indicators of collaboration, 7 indicators of industrial production, 4 indicators of dissemination, 8 indicators of health service impact. The most widely discussed and described is the h-index with 31 articles discussing it.DiscussionThe majority of indicators found are bibliometric indicators of scientific production and impact. Several indicators have been developed to improve the h-index. This indicator has also inspired the creation of two indicators to measure industrial production and collaboration. Several articles propose indicators measuring research impact without detailing a methodology for calculating them. Many bibliometric indicators identified have been created but have not been used or further discussed.
| S-EPMC4383328 | biostudies-literature
Project description:Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used for modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents the non-deep learning models that are commonly used for time series classification in biomedical applications that can achieve high performance. Objective: We performed a systematic review to characterize the techniques that are used in time series classification of digital clinical measures throughout all the stages of data processing and model building. Methods: We conducted a literature search on PubMed, as well as the Institute of Electrical and Electronics Engineers (IEEE), Web of Science, and SCOPUS databases using a range of search terms to retrieve peer-reviewed articles that report on the academic research about digital clinical measures from a five-year period between June 2016 and June 2021. We identified and categorized the research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: PubMed, IEEE, Web of Science Database, and SCOPUS. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely used machine learning classifiers were the most commonly used technique, and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also the second-best classification technique. Conclusions: In this review paper, summaries of the time series classification models and interpretation methods for biomedical applications are summarized and categorized. While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation, however many different methods have proven successful.
| S-EPMC9611376 | biostudies-literature
Project description:ObjectiveBiomedical text summarization helps biomedical information seekers avoid information overload by reducing the length of a document while preserving the contents' essence. Our systematic review investigates the most recent biomedical text summarization researches on biomedical literature and electronic health records by analyzing their techniques, areas of application, and evaluation methods. We identify gaps and propose potential directions for future research.Materials and methodsThis review followed the PRISMA methodology and replicated the approaches adopted by the previous systematic review published on the same topic. We searched 4 databases (PubMed, ACM Digital Library, Scopus, and Web of Science) from January 1, 2013 to April 8, 2021. Two reviewers independently screened title, abstract, and full-text for all retrieved articles. The conflicts were resolved by the third reviewer. The data extraction of the included articles was in 5 dimensions: input, purpose, output, method, and evaluation.ResultsFifty-eight out of 7235 retrieved articles met the inclusion criteria. Thirty-nine systems used single-document biomedical research literature as their input, 17 systems were explicitly designed for clinical support, 47 systems generated extractive summaries, and 53 systems adopted hybrid methods combining computational linguistics, machine learning, and statistical approaches. As for the assessment, 51 studies conducted an intrinsic evaluation using predefined metrics.Discussion and conclusionThis study found that current biomedical text summarization systems have achieved good performance using hybrid methods. Studies on electronic health records summarization have been increasing compared to a previous survey. However, the majority of the works still focus on summarizing literature.
| S-EPMC8449627 | biostudies-literature
Project description:Both passive and active microfluidic chips are used in many biomedical and chemical applications to support fluid mixing, particle manipulations, and signal detection. Passive microfluidic devices are geometry-dependent, and their uses are rather limited. Active microfluidic devices include sensors or detectors that transduce chemical, biological, and physical changes into electrical or optical signals. Also, they are transduction devices that detect biological and chemical changes in biomedical applications, and they are highly versatile microfluidic tools for disease diagnosis and organ modeling. This review provides a comprehensive overview of the significant advances that have been made in the development of microfluidics devices. We will discuss the function of microfluidic devices as micromixers or as sorters of cells and substances (e.g., microfiltration, flow or displacement, and trapping). Microfluidic devices are fabricated using a range of techniques, including molding, etching, three-dimensional printing, and nanofabrication. Their broad utility lies in the detection of diagnostic biomarkers and organ-on-chip approaches that permit disease modeling in cancer, as well as uses in neurological, cardiovascular, hepatic, and pulmonary diseases. Biosensor applications allow for point-of-care testing, using assays based on enzymes, nanozymes, antibodies, or nucleic acids (DNA or RNA). An anticipated development in the field includes the optimization of techniques for the fabrication of microfluidic devices using biocompatible materials. These developments will increase biomedical versatility, reduce diagnostic costs, and accelerate diagnosis time of microfluidics technology.
| S-EPMC9688231 | biostudies-literature
Project description:Although many methods have been developed for inference of biological networks, the validation of the resulting models has largely remained an unsolved problem. Here we present a framework for quantitative assessment of inferred gene interaction networks using knock-down data from cell line experiments. Using this framework we are able to show that network inference based on integration of prior knowledge derived from the biomedical literature with genomic data significantly improves the quality of inferred networks relative to other approaches. Our results also suggest that cell line experiments can be used to quantitatively assess the quality of networks inferred from tumor samples. Knock-down of eight genes were performed on colorectal cancer cell lines to identify the genes whose expression was significantly affected. These genes were subsequently used to validate the quality of our causal gene interaction network.
2013-12-23 | E-GEOD-53091 | biostudies-arrayexpress
Project description:BackgroundThe demand for high-quality systematic literature reviews (SRs) for evidence-based medical decision-making is growing. SRs are costly and require the scarce resource of highly skilled reviewers. Automation technology has been proposed to save workload and expedite the SR workflow. We aimed to provide a comprehensive overview of SR automation studies indexed in PubMed, focusing on the applicability of these technologies in real world practice.MethodsIn November 2022, we extracted, combined, and ran an integrated PubMed search for SRs on SR automation. Full-text English peer-reviewed articles were included if they reported studies on SR automation methods (SSAM), or automated SRs (ASR). Bibliographic analyses and knowledge-discovery studies were excluded. Record screening was performed by single reviewers, and the selection of full text papers was performed in duplicate. We summarized the publication details, automated review stages, automation goals, applied tools, data sources, methods, results, and Google Scholar citations of SR automation studies.ResultsFrom 5321 records screened by title and abstract, we included 123 full text articles, of which 108 were SSAM and 15 ASR. Automation was applied for search (19/123, 15.4%), record screening (89/123, 72.4%), full-text selection (6/123, 4.9%), data extraction (13/123, 10.6%), risk of bias assessment (9/123, 7.3%), evidence synthesis (2/123, 1.6%), assessment of evidence quality (2/123, 1.6%), and reporting (2/123, 1.6%). Multiple SR stages were automated by 11 (8.9%) studies. The performance of automated record screening varied largely across SR topics. In published ASR, we found examples of automated search, record screening, full-text selection, and data extraction. In some ASRs, automation fully complemented manual reviews to increase sensitivity rather than to save workload. Reporting of automation details was often incomplete in ASRs.ConclusionsAutomation techniques are being developed for all SR stages, but with limited real-world adoption. Most SR automation tools target single SR stages, with modest time savings for the entire SR process and varying sensitivity and specificity across studies. Therefore, the real-world benefits of SR automation remain uncertain. Standardizing the terminology, reporting, and metrics of study reports could enhance the adoption of SR automation techniques in real-world practice.
| S-EPMC11229257 | biostudies-literature
Project description:BackgroundConsiderable evidence on the costs and cost-effectiveness of biomedical, non-surgical interventions to prevent human immunodeficiency virus (HIV) transmission has been generated over the last decade. This study aims to synthesize findings and identify remaining knowledge gaps to suggest future research priorities.MethodsA systematic literature review was carried out in August 2020 using the MEDLINE, Embase, Global Health and EconLit databases to retrieve economic evaluations and costing studies of oral pre-exposure prophylaxis (PrEP), injectable long-acting PrEP, vaginal microbicide rings and gels, HIV vaccines and broadly neutralizing antibodies. Studies reporting costs from the provider or societal perspective were included in the analysis. Those reporting on behavioural methods of prevention, condoms and surgical approaches (voluntary medical male circumcision) were excluded. The quality of reporting of the included studies was assessed using published checklists.ResultsWe identified 3007 citations, of which 87 studies were retained. Most were set in low- and middle-income countries (LMICs; n = 53) and focused on the costs and/or cost-effectiveness of oral PrEP regimens (n = 70). Model-based economic evaluations were the most frequent study design; only two trial-based cost-effectiveness analyses and nine costing studies were found. Less than half of the studies provided practical details on how the intervention would be delivered by the health system, and only three of these, all in LMICs, explicitly focused on service integration and its implication for delivery costs. 'Real-world' programme delivery mechanisms and costs of intervention delivery were rarely considered. PrEP technologies were generally found to be cost-effective only when targeting high-risk subpopulations. Single-dose HIV vaccines are expected to be cost-effective for all groups despite substantial uncertainty around pricing.ConclusionsA lack of primary, detailed and updated cost data, including above-service level costs, from a variety of settings makes it difficult to evaluate the cost-effectiveness of specific delivery modes at scale, or to evaluate strategies for services integration. Closing this evidence gap around real-world implementation is vital, not least because the strategies targeting high-risk groups that are recommended by PrEP models may incur substantially higher costs and be of limited practical feasibility in some settings.
| S-EPMC10085926 | biostudies-literature
Project description:Large-scale transcriptome and methylome data analyses obtained by high-throughput technologies have been enabling the identification of novel imprinted genes. We investigated genome-wide DNA methylation patterns in multiple human tissues, using a high-resolution microarray to uncover hemimethylated CpGs located in promoters overlapping CpG islands, aiming to identify novel candidate imprinted genes. Using our approach, we recovered ~30% of the known human imprinted genes, further 168 candidates were identified, 61 of which with at least three hemimethylated CpGs shared by more than two tissue types. Thirty-four of these candidate genes are members of the protocadherins cluster on 5q31.3; in mice, protocadherin genes have non-imprinted monoallelic randomic expression, which might be the case in humans. Among the remaining 27 genes, ZNF331 was recently validated as an imprinted gene, and six of them have been reported as candidates, supporting our prediction. Five candidates (CCDC166, ARC, PLEC, TONSL and VPS28) map to 8q24.3, and might constitute a novel imprinted cluster. Additionally, we performed a comprehensive compilation of known human and mice imprinted genes from literature and databases, and a comparison among high-throughput imprinting studies in humans. The screening for hemimethylated CpGs shared by multiple human tissues, together with the extensive review, appears as a useful approach to reveal candidate imprinted genes.
2017-09-26 | GSE103413 | GEO